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Many applications of machine learning, such as human health research, involve processing private or sensitive information.
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2012
Cited alongside, same era.
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2013
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2013
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2013
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2013
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2014
Cited alongside, same era.
2014
Cited alongside, same era.
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2014
Cited alongside, same era.
F. Eigner, A. Kate, M. Maffei, F. Pampaloni, and I. Pryvalov, “Differentially Private Data Aggregation with Optimal Utility,” in Proceedings of the 30th Annual Computer Security Applications Conference , ser. ACSAC ’14. New York, NY, USA: ACM, 2014, pp. 316–325
2014
Cited alongside, same era.
P. M. Thompson, O. A. Andreassen, A. Arias-Vasquez, C. E. Bearden, P. S. Boedhoe, R. M. Brouwer, R. L. Buckner, J. K. Buitelaar, K. B. Bulayeva, D. M. Cannon et al. , “ENIGMA and the Individual: Predicting Factors that Affect the Brain in 35 Countries Worldwide,” Neuroimage , vol. 145, pp. 389–408, 2017
2017
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2017
Later among the works it cites.
S. Han, U. Topcu, and G. J. Pappas, “Differentially Private Distributed Constrained Optimization,” IEEE Transactions on Automatic Control , vol. 62, no. 1, pp. 50–64, Jan 2017
2017
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K. Ligett, S. Neel, A. Roth, B. Waggoner, and S. Z. Wu, “Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM,” in Advances in Neural Information Processing Systems 30 , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 2563–2573
2017
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D. Wang, M. Ye, and J. Xu, “Differentially Private Empirical Risk Minimization Revisited: Faster and More General,” in Advances in Neural Information Processing Systems 30 , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 2719–2728
2017
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R. Bassily, K. Nissim, U. Stemmer, and A. Guha Thakurta, “Practical Locally Private Heavy Hitters,” in Advances in Neural Information Processing Systems 30 , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 2288–2296. [Online]. Available: http://papers.nips.cc/paper/6823-practical-locally-private-heavy-hitters.pdf
2017
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2017
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M. Heikkilä, E. Lagerspetz, S. Kaski, K. Shimizu, S. Tarkoma, and A. Honkela, “Differentially Private Bayesian Learning on Distributed Data,” in Advances in Neural Information Processing Systems 30 , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 3229–3238
2017
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2018
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J. Zhu, C. Xu, J. Guan, and D. O. Wu, “Differentially Private Distributed Online Algorithms Over Time-Varying Directed Networks,” IEEE Transactions on Signal and Information Processing over Networks , vol. 4, no. 1, pp. 4–17, March 2018
2018
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C. Li, P. Zhou, L. Xiong, Q. Wang, and T. Wang, “Differentially Private Distributed Online Learning,” IEEE Transactions on Knowledge and Data Engineering , vol. PP, no. 99, pp. 1–1, 2018
2018
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2018
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2018
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J. So, B. Guler, A. Salman Avestimehr, and P. Mohassel, “CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning,” arXiv e-prints , Feb 2019
2019
Closest in time.
E. Nozari, P. Tallapragada, and J. Cortés, “Differentially Private Distributed Convex Optimization via Objective Perturbation,” in 2016 American Control Conference (ACC) , July 2016, pp. 2061–2066
2066
Closest in time.